Frequent itemset mining (FIM) is a family of algorithms sometimes known as market basket analysis (MBA) or association rules learning. The family of algorithms are intended to discover relations or rules between items in large datasets in business analysis, marketing, or other applications. For example, the algorithms, applied to transaction data collected at a supermarket, can identify association relations or rules among products purchased by customers. Based on the association rules, custom behaviors can be analyzed and facilitate product promotion and sales. Some typical FIM algorithms include, for example, the APRIORI algorithm, the ECLAT algorithm, the FP-growth algorithm, and the linear time closed itemset miner (LCM) algorithm.